Machine Learning Interview Questions In 2022

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Top 19 Machine Learning Interview Questions
1. Why reasons resulted in Machine learning
introduction? The simplest answer is for making
our lives easier. In the early days of
intelligent applications, numerous systems
depended on hardcode rules of if and else
decisions for processing data or adjusting the
user input. Imagine spam filter whose job is to
move the right incoming email messages to a spam
folder. With machine learning algorithms, one is
offered ample information for the data to learn
and identify patterns from the data. One is not
required to write new rules for each problem in
machine learning. 2. What are several Types of
Machine Learning algorithms? There are several
machine learning algorithms. Broadly speaking
Machine learning algorithms are divided in
supervised, unsupervised, and reinforcement
learning.
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• 3. What is Supervised Learning?
• Supervised learning simply putmachine learning
  algorithm of deducing a function from labelled
  training data. Some of the supervised learning
  algorithms are
• Support Vector Machines
• Regression
• Naive Bayes
• Decision Trees
• 4. What is Unsupervised Learning?
• Unsupervised learning is second type of ML
  algorithm considered for finding patterns on the
  set of data provided. In this one does not have
  to dependent on variable or label to predict.
• Unsupervised learning algorithms include
• Clustering,
• Anomaly Detection,
• Neural Networks and Latent Variable Models.
• In case you wish to gain more clarity
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5. What is Naive concept in Naive Bayes? Naive
Bayes methodology is a supervised learning
algorithm it is naive as it makes supposition by
applying Bayes theorem that all characteristics
are independent of each other. Consult a machine
learning bootcamp to understand the technique and
further tools for cracking the interview.
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6. What is PCA? When do you use it?Principal
component analysis (PCA) is the most commonly
used for dimension reduction and measures the
variation in each variable. If there is little
alteration, it throws the variable out.Principal
component analysis makes the dataset easy to
visualize, and is used in finance, neuroscience,
and pharmacology. It is further useful in
pre-processing stage, when linear correlations
are present between features. Consider coding
bootcamp for learning tools and techniques.7.
Explain SVM Algorithm.A SVM or Support Vector
Machine is a strong and versatile supervised
machine learning model, capable of performing
linear or non-linear classification, outlier
detection and regression.
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• 8. What are Support Vectors in SVM?
• Support Vector Machine (SVM) is an algorithm
  which makes fitting line between different
  classes that maximizes the distance from line to
  the points of the classes. In this manner, it
  tries to find a robust separation between
  classes. Support Vectors are points of edge of
  dividing hyper plane.
• 9. What are Different Kernels in SVM?
• There are 6 types of kernels in SVM however,
  following four are widely used
• Linear Kernel- used when data is linearly
  separable.
• Polynomial kernel When one has discrete data
  that has no natural notion of efficiency.
• Radial basis kernel Is used for creating a
  decision boundary for doing a better job of
  separating two classes compared to the the linear
  kernel.
• Sigmoid kernel Is used as an activation
  function for neural networks.

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• 10. What is Cross-Validation?
• Cross-Validation is a method of splitting data in
  three parts- training, validation and testing.
  Data is split into K subsets, and models have
  trained on k-1 of the datasets. The last subset
  is held for testing and is conducted for each of
  the subsets. This is k-fold cross-validation.
  Lastly, the scores from all the k-folds are
  averaged for producing final score.
• 11. What is Bias in Machine Learning?
• Bias in data indicates there is inconsistency in
  data. The inconsistency may be cause due to
  several reasons which are not reciprocally
  exclusive.

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• 12. What is the Difference Between Classification
  and Regression?
• Classification is used for producing discrete
  results whereas, classification is used for
  classifying data into some definite categories.
• 13. Define Precision and Recall?
• Precision and recall are ways of monitoring power
  of machine learning implementation. But these are
  often used at the same time. Precision may
  inspect relevance whereas recall answers the
  questions. Basically, the meaning of precision is
  the fact of being exact and accurate. Same goes
  in machine learning models as well. In case one
  has set of items that model needs to predict to
  be relevant then it could answer how many items
  are truly relevant.

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• 15. How to Tackle Overfitting and Underfitting?
• Overfitting means model fitted for training data
  well, in this case, one needs to resample the
  data and estimate model accuracy using techniques
  like K-fold cross-validation. Whereas in case of
  underfitting one is not able to understand or
  capture the patterns from data, in such case, one
  needs to change the algorithms, or one needs to
  feed more data points in the model for accuracy.
• 16. What is a Neural Network?
• Neural Network to put in simple words is model of
  human brain. Much like brain, it has neurons that
  activate when encountering something relatable.
  Different neurons are connected via connections
  which help information flow from one neuron to
  another.

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• 17. What is Ensemble learning?
• Ensemble learning is a method that joins multiple
  machine learning models for creating powerful
  models.
• There are numerous reasons for a mode to be
  different. Some are
• Different Hypothesis
• Different Population
• Different Modelling techniques
• When working with models training and testing
  data, one can experience an error. This error
  might be bias, irreducible error or variance.
• Now model should have a balance between bias and
  variance, this one call a bias-variance
  trade-off. This ensemble learning is a manner to
  perform this trade-off. There are numerous
  ensemble techniques available but when
  aggregating multiple models there are general two
  methods- Bagging and Boosting.

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• 18 . How does one make sure which Machine
  Learning Algorithm to use?
• It solely depends on the dataset one has. If the
  data is discrete one makes use of SVM. If the
  dataset is continuous one uses linear regression.
  So, while there is no specific way for knowing
  which ML algorithm to use, it entirely depends on
  the exploratory data analysis (EDA)

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• 19. How to Handle Outlier Values?
• An outlier is an action in the dataset which is
  far away from other observations in the dataset.
  Tools used for discovering outlier are
• Z-score
• Box plot
• Z-score
• Scatter plot
• Conclusion,
• The above listed questions cover the basics of
  machine learning. With the advancement in machine
  learning growing rapidly so in case one has to
  consider joining the communities, and cracking
  the interview machine learning bootcamp is the
  way forward.
• Source https//jenahaley54.medium.com/top-19-mach
  ine-learning-interview-questions-addee3317084